Search bioRxiv⌕ Search

Biology subjects

Chausse, J.

Publications and source records attributed to Chausse, J..

2 recordsLinked to original sources

Evaluating BOLD functional MRI biophysical simulation approaches: impact of vascular geometry, magnetic field calculations, and water diffusion models

Biophysical simulations have guided the development of blood oxygenation level-dependent (BOLD) functional MRI (fMRI) acquisitions and signal models that relate the BOLD signal to the underlying physiology, such as calibrated BOLD and vascular fingerprinting. Numerous simulation techniques have been developed, however, few of them have been directly compared, thus limiting the assessment of the accuracy and interchangeability of these methods as well as the accuracy of the quantitative techniques derived from them. In this work, we compared the accuracy and computational demands of eight previously published simulation approaches that adopt different geometries (ranging from infinite cylinders to synthetic vascular anatomical networks (VANs)), field offset calculations (analytical and Fourier-based), and water diffusion implementations (Monte Carlo and convolution-based), all of which are available in an open-source Python toolkit, BOLDs{omega}imsuite. The reference simulation approach for comparison used three-dimensional infinite cylinders, analytical field offsets, and Monte Carlo diffusion. When compared with the reference approach, most of the simulations, including two- and three-dimensional geometries, were in excellent agreement when assuming the intravascular signal contribution was small. Two commonly employed simulation approaches were notably biased; both used two-dimensional geometries with overly simplified vasculature or field offset calculations. In general, the simulated intravascular signal was the least consistent across approaches, thus potentially resulting in larger errors when the intravascular signal contribution is large. Lastly, the VAN results were in good agreement with the reference but they diverged slightly, yet systematically, from each other at smaller radii ([lsim] 3 m), primarily driven by intravascular signal differences. We conclude, therefore, that the reference approach is an attractive option for exploratory simulations in the many cases where anatomical and hemodynamic realism is not needed, balancing ease of implementation, accessibility, versatility, computational efficiency, accuracy of results, and interpretability. These findings help pave the way for a broader adoption of forward modelling of the BOLD signal and more reliable interpretations of biophysical simulations aiming to develop quantitative models of the BOLD signal.

neuroscience↗

BOLDsωimsuite: A new software suite for forward modeling of the BOLD fMRI signal

In trying to elucidate the mechanisms of the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) techniques and to expand on the potential of the transverse relaxation time (T2*) in quantitative MRI, many methods for the forward modelling of the BOLD effect have been created and analyzed. Simulations of this nature can be difficult to implement without prior experience, and differences made by methodological choices can be unclear, which provides a significant barrier of entry into the field. In this paper, we present BOLDswimsuite, a toolbox for forward modeling of the BOLD effect, which collects many of the principal methods used in the literature into a single coherent package. Implemented as a Python package, simulations are built-in scripts by combining various simulation components, while providing flexibility in methodological choices. The goal of this toolbox is to provide an open-source, reproducible software suite that is adaptable for simulations in different MRI applications, and to which additional features can be added by the user with relative ease. This paper first provides an overview of the methods available in the package and how these methods can be constructed from the toolboxs modular code components. Then, a brief theoretical explanation of each simulation component is given, supported by the relevant contributors. Next, sample simulations and analyses that can be created using the package are presented to display its features. Finally, recommendations regarding computational requirements are included to help users choose the best simulation methods to fit their needs. This package has many use cases and significantly reduces methodological barriers to forward modeling. It can also be a good learning tool for MR physics as well as a powerful tool to promote reproducible science.

biophysics↗